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ent from previous work because Dr. Leinauer
was especially interested in turfgrass quality, a
measure that includes density, uniformity, leaf
texture, smoothness, growth habit and green
color. Previously, most of the work in this area
focused on turfgrass color only. In this study,
as with previous studies, the researchers found
signifcant correlation in turf color data col
-
lected by the NDVI and DIA color-index
methods, and in percent cover data collected
through NDVI and DIA. Thus, both types
of remote-sensing equipment seemed to agree
with each other pretty well, when color and
percent cover were assessed.
But what about the quality of the turf —
that multicharacteristic rating of the attrac
-
tiveness of a sward? Well, the NDVI meter
was best for tracking changes over time. That
is, from day to day, NDVI readings might be
the best tool for describing changes in turf
-
grass quality over time — even better than a
person's visual ratings. Thus, an NDVI meter
could be a handy tool for your course, al
-
lowing you to track turfgrass quality in your
specifc grass and management situation over
time. The NDVI and DIA tools were less use
-
ful when different varieties were being com-
pared. In that case, visual assessments best
detected differences caused by variety. So,
NDVI readings you collect from your course,
with your variety, would not be especially use
-
ful to your neighbor at a different course with
a different variety of turf. At this time, the au
-
thors concluded that a visual assessment de-
tected quality differences in turfgrasses more
accurately, especially when different varieties
were involved.
Source: Leinauer, B., D.M. VanLeeuwen, M.
Serena, M. Schiavon and E. Sevostianova.
2014. Digital image analysis and spectral
refectance to determine turfgrass quality.
Agronomy Journal 106:1787-1794.
Beth Guertal, Ph.D., is a professor in the department of
crop, soil and environmental sciences at Auburn Univer
-
sity in Auburn, Ala., and the editor-in-chief for the Ameri-
can Society of Agronomy. She is an 18-year member of
GCSAA.
98 GOLF COURSE MANAGEMENT 03.15
Remote sensing. Digital imagery. Spectral
refectance. All terms used to describe some
basic procedure by which a digital image is
used to quantify the color or growth of turf
-
grass. The science has made its way into gen-
eral use, and now everything from relatively
cheap hand-held sensors to affordable cell
phone applications can be purchased to help
keep track of the color of your turfgrass. Some
of these tools have been shown to work well
in rating and tracking percent green cover and
turfgrass color.
But what about quality? Although the
color of turfgrass is certainly a primary factor
in determining turf quality, a lot of other char
-
acteristics can often affect quality. The abil-
ity of remote sensing to evaluate the quality
of turf has not been well studied, and the re
-
sults that are out there are pretty mixed. So, in
an effort to gain some more defnitive results,
the folks at New Mexico State University (Dr.
Bernd Leinauer and his crew) used the Na
-
tional Turfgrass Evaluation Program (NTEP)
variety trials to try to get a handle on the re
-
mote sensing of turfgrass quality. They used
bermudagrass, zoysiagrass, seashore paspalum,
Kentucky bluegrass and tall fescue variety tri
-
als. Over four years, they took monthly visual
ratings (using a standard 1–9 scale, where 1
indicates dormant or dead, 9 is perfect and
6 is minimally acceptable), NDVI (normal
-
ized difference vegetative index) readings
and DIA (digital image analysis) readings.
NDVI readings are a refectance (in the near-
infrared and red ranges) obtained from scan
-
ning an area of turf, while DIA readings are
photographs taken under controlled settings
(camera mounted on a metal box that encom
-
passes a known area of turf ), which are then
digitized to a green color index. In this study,
the DIA readings were also used to obtain a
percent cover rating. The idea of these alterna
-
tive measurement methods is that a quick scan
or picture of a turfgrass sward would provide
an accurate and unbiased estimate of turfgrass
quality, an estimate not sullied by a human's
perceptions or preconceptions.
At the simplest, Dr. Leinauer wanted to
see how well visual quality, NDVI and DIA
related to each other. This study was differ
-
Beth Guertal, Ph.D.
guertea@auburn.edu
twitter: @AUTurfFert
I see you doing that
(verdure)
This study was
different from
previous work
because
Dr. Leinauer was
especially interested
in turfgrass quality,
a measure that
includes density,
uniformity,
leaf texture,
smoothness,
growth habit and
green color.